Instruction-following language models demand robust methodologies for information retrieval to augment instructions for question-answering applications. A primary challenge is the resolution of coreferences in the context of chunking strategies for long documents. The critical barrier to experimentation of handling coreferences is a lack of open source datasets, specifically in question-answering tasks that require coreference resolution. In this work we present our Coreference Resolution in Question-Answering (CRaQAn) dataset, an open-source dataset that caters to the nuanced information retrieval requirements of coreference resolution in question-answering tasks by providing over 250 question-answer pairs containing coreferences. To develop this dataset, we developed a novel approach for creating high-quality datasets using an instruction-following model (GPT-4) and a Recursive Criticism and Improvement Loop.

In recent years, we can witness that artificial intelligence is becoming a need in every domain of the industry, and AI's different domains, such as computer vision, natural language processing, and predictive modelling, are helping humans solve their use cases and problems more effectively and without the intervention of the humans. We can also enjoy the intervention of AI in our daily life, and humans are becoming more curious about this intervention. Banking sectors are also positively affected by the intervention of AI. In this article, we will cover some of the critical use cases of AI in the banking sector that is helping humans advance the banking sector. This sector implies AI-enabled models to assist the customer during onboarding.

Humans possess a powerful ability to reason. They understand a question asked by a fellow human-being and provide the most appropriate answer to it. A human brain can do quick mathematics to answer a trivial question like "If I have 10 balls and bought two cans, each having 5 balls, how many balls would I have?" The humans can do commonsense reasoning like "If a driver sees a pedestrian on the crossover, what would he do?" Humans have intelligence in understanding if somebody is cutting a joke and probably get a deeper understanding of what the speaker really wants to say? The question is, can we train the machines to gain this kind of intelligence that we humans possess?

"The idea of the three-box solution has its roots in Hindu spirituality," explains Govindarajan. "The ancient scriptures portray life as a continuous cycle of preservation, destruction, and creation. Every entity in the universe invariably passes through these three phases." We've seen how the principles of the three-box solution, inspired by 5,000-year-old texts, are relevant for companies today. To build immortal companies, you must master this preservation, destruction, and creation cycle. "It's a mission that's never fully accomplished because change is the only constant," concludes Govindarajan. You can watch my full interview with Professor Vijay Govindarajan on how the three-box solution helps address the biggest challenges in building an AI-powered organization.

Digital Humanities is an emerging research area. Wikipedia says that "Digital Humanities (DH) is an area of scholarly activity at the intersection of computing or digital technologies and the disciplines of the humanities." This statement doesn't fully reveal anything clear or concrete. Whatever the definition, it is important to develop more services where humans and machines work together better. In most cases, AI is not an independent machine that handles all tasks but a tool to help people. That's why we need research and development to get this interaction working better.

There's no doubt that the finance industry is undergoing a transformational change. The recent years have seen a rapid acceleration in the pace of disruptive technologies such as AI and Machine Learning in Finance due to improved software and hardware. The finance sector, specifically, has seen a steep rise in the use cases of machine learning applications to advance better outcomes for both consumers and businesses. Until recently, only the hedge funds were the primary users of AI and ML in Finance, but the last few years have seen the applications of ML spreading to various other areas, including banks, fintech, regulators, and insurance firms, to name a few. Right from speeding up the underwriting process, portfolio composition and optimization, model validation, Robo-advising, market impact analysis, to offering alternative credit reporting methods, the different use cases of AI and Machine Learning In Finance are having a significant impact on this sector.

This drastic reduction in latency alone makes a number of futuristic technologies – such as autonomous vehicles – possible. The advent of cloud computing set off a colossal centralization fever that has caught almost every business that understands the importance of a digital-first business strategy. Even the world's governments and public sector organizations are leveraging the advantages offered by cloud computing. Easy access to data, powerful analytical tools, and improved business agility have enabled organizations to make more "intelligent" and informed decisions than ever before. However, over the next few years, a rival computing architecture approach – decentralization – will witness a sharp uptick in popularity, fueled by edge computing.

Artificial Intelligence (AI) has become one of the most popular buzzwords in the IT industry today. It's boosted the confidence of tech pundits but also instilled fear in the hearts of the salaried professionals and small business owners. Some businesses believe that AI technology is coming after their jobs and companies. Whether you are in favor of the widespread implementation of AI or not, the technology is here to say. It's matured considerably over the past few years, and new-age solutions are already being devised and implemented across various industries.

In 1971, Terry Winograd wrote the SHRDLU program while completing his PhD at MIT. SHRDLU features a world of toy blocks where the computer translates human commands into physical actions, such as "move the red pyramid next to the blue cube." To succeed in such tasks, the computer must build up semantic knowledge iteratively, a process Winograd discovered was brittle and limited. The rise of chatbots and voice activated technologies has renewed fervor in natural language processing (NLP) and natural language understanding (NLU) techniques that can produce satisfying human-computer dialogs. Unfortunately, academic breakthroughs have not yet translated to improved user experiences, with Gizmodo writer Darren Orf declaring Messenger chatbots "frustrating and useless" and Facebook admitting a 70% failure rate for their highly anticipated conversational assistant M. Nevertheless, researchers forge ahead with new plans of attack, occasionally revisiting the same tactics and principles Winograd tried in the 70s. OpenAI recently leveraged reinforcement learning to teach to agents to design their own language by "dropping them into a set of simple worlds, giving them the ability to communicate, and then giving them goals that can be best achieved by communicating with other agents."

The chatbot hype was mainly due to the argument that instant messaging channels have become the de facto user browser at Mobile over the last few years. With the high volume of active users, messaging platforms started integrating product and services from 3rd party partners. With the success of Wechat in China, it was easy to imagine a bright future of Chatbot. Quick forward to today, one year later after Facebook launched the Messenger platform for business, chatbots still haven't replaced apps. The main problem is the approach that Facebook and many other companies took: they were over optimistic about the state of AI technologies (mainly NLP), which are still too incipient for most business use cases.